http://idtv.det.uvigo.es/es/avatar/ontologia.html. (Blanco-Fernández et al., 2004b), used by AVA-. TAR. Concerning the second one, in next sections, we propose ...
Entercation Experiences: Engaging Viewers in Education through TV Programs* Marta Rey-López, Rebeca P. Díaz-Redondo, Ana Fernández-Vilas and José J. Pazos-Arias Department of Telematic Engineering University of Vigo, 36310, Spain {mrey, rebeca, avilas, jose}@det.uvigo.es
Abstract IDTV (Interactive Digital TV) opens new learning possibilities where new forms of education are needed. In order to achieve the participation of viewers in these experiences —overcoming their typical passivity— the combination of education and entertainment is essential. In this paper, we present a new concept in t-learning: entercation experiences, where we use TV programs as a hook to engage users in education. The key element of the appropriate scenario for this approach is an ontology for learning contents based on the ADL SCORM (Sharable Content Object Reference Model) standard. We have developed this ontology which will permit choosing the appropriate elements for constructing those experiences, providing learning personalization.
1. Introduction New information technologies have come along with new learning possibilities, reaching those social groups that would hardly have access to traditional forms of education. This new training strategies can be grouped together under the term elearning. Although this term is commonly used in reference to learning accomplished over the Internet, it can adopt different appearances: m-learning (for mobile devices), t-learning (for television), etc. The term t-learning has been adopted as shorthand to mean TV-based interactive learning (pjb Associates, 2003). It is not just an adaptation for IDTV (Interactive Digital TV) of the e-learning techniques used in the Internet. T-learning has its own distinctive characteristics, mostly related to the constraints imposed by this media, such as the low resolution of the screen, the fact of using a simple remote control to interact with the programs or the reduced features of a set-top box compared with a computer.
(traditionally used in television) reducing the appearance of text to the minimum, since it will be difficult for the user to read it. As important as technical features are social ones, such as the predisposition of the student to education. In e-learning, the student gets generally involved in learning experiences on his own initiative, whereas in t-learning he/she is usually more passive. That is why t-learning students should be attracted into education by means of entertaining activities they could find interesting. The term edutainment (education that entertains) has been commonly used in IDTV meaning a form of entertainment designed to be educational. Our proposal goes further, as its purpose is using TV programs as an entrance to education, providing learning contents related to the programs. To refer to these new learning experiences we have coined the term entercation 1 (entertainment that educates). In order for these experiences to be successful, we firstly need to identify those characteristics of the programs which could arouse viewer’s curiosity and then select the appropriate learning contents to satisfy this curiosity. To fulfil the first necessity, an ontology is needed so as to classify TV programs and store their most relevant characteristics. Concerning the second one, another ontology is required to reason over the characteristics of those learning objects available and find relationships between them and TV programs. TV programs offered to the user are chosen by a recommender system based on semantic reasoning, called AVATAR (Blanco-Fernández et al., 2004a), that our working group has been developing. This recommender selects audiovisual contents that are broadcasted according to the DVB-Multimedia Home Platform (MHP) (DVB Consortium, 2003) standard, which uses MPEG-2 transport streams to *
These characteristics impose a different conception of learning objects. As opposed to e-learning ones, they should principally consist of audio and video
Partly supported by the R+D project TSI 2004-03677 (Spanish Ministry of Education and Science) and by the EUREKA ITEA Project PASSEPARTOUT. 1 Although this term has been previously used, it did not have the connotation of attraction to education we propose.
package and multiplex the contents. Concerning applications, they are mounted over a structure called Object Carousel (ISO/IEC, 1998), which is repeatedly and periodically broadcasted, offering a local filesystem in the user’s set-top box. In a parallel direction, we are currently designing an Intelligent Tutoring System (ITS) for IDTV, called t-MAESTRO (Multimedia Adaptive Educational SysTem based on Reassembling TV Objects), which is in charge of creating the entercation experiences to complement TV programs with educational contents. This ITS works with learning elements conformant with the ADL SCORM (Sharable Content Object Reference Model) (ADL SCORM, 2004) standard. In this paper, we present a new concept for learning through IDTV where TV programs are used as a hook to attract the viewer to a learning experience, called entercation (Sec. 2). Then we expose, in Sec. 3, the scenario where this proposal takes place. In Sec. 4 we suggest an ontology based on SCORM specifications, which will allow to store the information related to learning objects and to establish semantic relationships between them and TV programs. The mechanism of selection of educational contents to complement a TV program is explained in Sec. 5. For a better understanding of this mechanism, we propose a little example of the creation of an entercation experience in Sec. 6. Finally we discuss the related work in Sec. 7 and expose the conclusions and further work in Sec. 8.
central axis the TV program the user is watching and consist of a set of educational elements (interactive programs, video, text...) related to that one to a certain extent. Learning element
Learning element
Learning element
Learning element
TV program
Learning element
Learning element
Learning element
Learning element
Figure 1: Entercation experiences
We expose next an example to illustrate how entercation experiences are built (see Figure 2). Let us suppose that AVATAR has selected the film Under the Tuscan sun. To create an entercation experience with this film as central axis, learning objects related to those characteristics of the film which can arouse the user’s curiosity should be selected. These characteristics could be that the story takes place in Tuscany and in the Italian Amalfi Coast or that some of the dialogs are spoken in Italian. The appropriate objects could be the elements of a course of art that refer to Tuscany, a documentary about Campania Region (where Amalfi Coast is), as well as an Italian course. These objects will be offered to the viewer who can decide whether to study them or not.
2. Entercation experiences In 1973, Robert Heyman coined the term edutainment (mixing education and entertainment) while producing films for the National Geographic Society, meaning a form of entertainment (as by games, films or shows) designed to be educational. Considering the typical passivity of the t-learning student aforementioned, edutainment is very suitable for education in IDTV. However, our proposal goes further, since it consists not only in introducing educational contents in TV programs, but also in using these ones as a hook to attract viewers to education. To refer to this form of learning experiences, we coin the term entercation. These experiences permit to offer to the viewer educational content related to the matter of the program he/she is watching, being free to decide whether studying this additional material or not. Entercation experiences for t-learning lead to a less formal education form where its contents are not structured. As we can see in Figure 1, they have as
Figure 2: Example of an entercation experience
If the user decides to study some of the objects provided, we would have managed to involve him/her in a learning experience from a TV program intended for entertainment, achieving the objective proposed in entercation.
IDTV Transport Stream
Audiovisual Content Filter
Viewer Viewer Profile Profile
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Domain Domain ontology ontology
AVATAR
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Student Student Profile Profile
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SCORM SCORM ontology ontology
g
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Entercation course LE
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Figure 3: Scenario
In the example above, when selecting the most appropriate learning contents, we have only took into account the relationships between educational objects and TV programs. This selection mechanism could produce the opposite effect in the user, since learning objects —although related to the matter of the film— may not suit its preferences or learning background. For example, if the viewer has a medium level in Italian and we offer him/her a course for beginners, he/she will probably get bored and will not be engaged in the learning experience. However, if we recommend to the user a proper course for his/her level, the probability that he/she studies the course is clearly increased. On the other hand, the viewer’s preferences are also important, if we offer him/her part of an art course and he/she does not like art, the objectives will not be fulfilled either. Due to the exposed reasons, user’s preferences and learning background should also be taken into account in order to select the appropriate learning contents for each user and personalize the learning experience. It is advisable not only to provide the viewer with contents selection, but also with contents personalization in order to make education more attractive —since contents will adapt to his/her preferences— and effective —because they will be appropriate to
his/her previous learning background. This is the key factor of the user model, which consists of two separate profiles in an IDTV environment: viewer profile and student profile, as we will explain in the next section.
3. Application scenario After having explained in previous sections the objective of this proposal, we present in this section the scenario where entercation experiences will be created, shown in Figure 3. In this scenario we can see two different types of elements: those which participate in the operation of the system and the ones which are needed to carry out the reasoning process. Concerning the first ones, AVATAR is in charge of selecting the most appropriate TV programs (in the figure, TV) for the viewer, whereas tMAESTRO has to perform all the tasks needed to select those learning elements (in the figure, LE) that better suit the program’s and user’s characteristics. With reference to the reasoning process, three ontologies are needed to create entercation experiences: the Domain Ontology —to establish relationships between concepts—, the TV ontology — to store the information related to TV programs— and an ontology to act as a repository for informa-
tion related to available learning elements, based on the SCORM standard, the one we have chosen to structure and broadcast those elements.
3.1 Operation of the system As in traditional analogue TV, the client can receive in his/her set-top box, traditional audiovisual contents, i.e. TV programs. On the other hand, some thematic channels may broadcast learning contents related to its objectives, parallel to programs, and some channels could be only dedicated to educational aims. For this reason, filtering transport stream is necessary in order to separate contents (step n), so as TV programs will be sent to AVATAR, whereas educational contents will be forwarded to the Educational Recommender. Next, selection of contents should be done (step o): AVATAR chooses those TV programs considered interesting for the user according to his/her viewer profile, while the Educational Recommender decides which learning objects suits his/her student profile, having into account his/her level, former learning experience, preferences, etc. They both use the Domain Ontology to establish semantic relationships between concepts. Using the contents selected by both recommenders, the Assembler composes the entercation experience (step p). It has to select the appropriate learning objects for the TV programs that will be offered to the viewer, relating the TV Ontology with the SCORM one by means of the Domain Ontology so that characteristics of learning objects are semantically related to TV programs’ ones. After that, the contents are linked to be sent to the user, and a directory is created to indicate from what moment on the learning elements are available in the TV program. Finally (step q), the LMS (Learning Management System) 2 receives the contents and shows them to the viewer. The LMS has also the mission of receiving feedback information from the user that makes possible updating student and viewer profiles (steps g).
fered to the user accompanying the TV programs takes place in two steps. Firstly, those which do not suit the student profile are rejected. Secondly, those ones more related to the program which centres the entercation experience are chosen among those kept. This way we will achieve, on the one hand, reducing the complexity of the task carried out by the Assembler and, on the other hand, to rule out the possibility of offering the user learning objects that do not suit his/her student profile. For the project we are working on, we have defined a toy user profile, whose main attribute is that it takes into account both his/her characteristics as a viewer and a learner and it stores his/her preferences, educational level and history. Let us talk now about the ontologies needed to carry out this process. Firstly, as aforementioned, we need a Domain Ontology which permits to establish semantic relationships between concepts, in order to permit that filtering and selection were made by semantic reasoning. For our project, we could use, for example, SUMO (Suggested Upper Merged Ontology) (Niles et al., 2001). This ontology is being created as part of the IEEE Standard Upper Ontology Working Group, whose goal is to develop a standard upper ontology that will promote data interoperability, information search and retrieval, automated inferencing, and natural language processing. On the other hand as mentioned in Sec. 1, we need an ontology to act as a repository for TV programs and their more relevant characteristics, to the purpose of allowing AVATAR to select the most suitable programs for the user and the Assembler to obtain its most relevant characteristics, in an effort to select the most appropriate educational contents for them. In order to carry out this task, a second ontology is needed, which will store the instances of those educational objects that can be accessed at a concrete moment, their characteristics and interrelations. With reference to the first one, we will use the ontology of TV programs available at http://idtv.det.uvigo.es/es/avatar/ontologia.html (Blanco-Fernández et al., 2004b), used by AVATAR. Concerning the second one, in next sections, we propose a SCORM-based ontology that will allow us to fulfil the exposed requirements.
3.2 Reasoning of the system As we can see in the model we are explaining, the selection of educational elements that will be of2
In e-learning terminology, the term LMS is used to refer to the system designed to deliver, track, report on and manage learning content, learner progress and learner interactions.
4. Learning contents and the SCORM ontology In this section, we describe how the educational contents received by the users are organized and we present the ontology we use for carrying out their selection.
THING
SCORM ELEMENT CATEGORY
hasGeneral (1:1) hasLifeCycle (1:1) hasMetaMetaData (1:1) hasTechnical (1:1) hasEducational (1:1) hasRights (1:1) hasAnnotation (0:30) hasClassification (0:40)
LOM CATEGORY
GENERAL isPartOf (0:100) hasPart (0:100) isVersionOf (0:100) hasVersion (0:100) isFormatOf (0:100) hasFormat (0:100) references (0:100) isReferencedBy (0:100) isBasedOn (0:100) isBasisFor (0:100) requires (0:100) isRequiredBy (0:100)
LIFECYCLE
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EDUCATIONAL interactivityLevel difficulty ...
SEQUENCING
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CLASSIFICATION taxon entry ... FILE
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hasSequencing hasGeneral (0:1) hasLifeCycle (0:1) hasTechnical (0:1) hasRights (0:1)
hasAsset
ASSET hasLifeCycle (0:1)
hasTitle (String) hasResource
hasAsset
hasItem
hasLocation
hasItem
hasOrganization hasSequencing
hasTimeLimitAction needsDataFromLMS hasCompletionThreshold hasPresentation
Figure 4: Main classes and properties in the ontology
We have to emphasize the importance of standardization, since it permits reusing learning contents, as well as making those ones independent from the hardware and software in the user’s set-top box. As we have already mentioned, we have chosen the SCORM standard for the educational content to be conformant with, because it references specifications, standards and guidelines developed by other organizations that are adapted and integrated with one another to form a more complete and easier-toimplement model. As introduced in Sec. 1, to achieve the selection of contents required to create entercation experiences, an ontology is needed to act as a repository for the information related to learning elements tMAESTRO has access to, as well as for their interrelations. This ontology is based on the SCORM standard, the one we use for defining and broadcasting educational contents.
4.1 The SCORM standard The SCORM standard is divided into technical books grouped under three main topics: SCORM Content Aggregation Model (CAM), covering assembling, labelling and packaging of learning content; SCORM Sequencing and Navigation (SN), describing how educational content may be sequenced through a set of navigation events; and SCORM Run-time Environment (RTE), whose purpose is providing a means for interoperability between SCOs (Sharable Content Objects) and LMSs. SCORM defines, in SCORM CAM book, five different components used to build a learning experience from learning resources (as shown in Figure 5) that can be accompanied by IEEE Learning Object Metadata (LOM) (IEEE LTSC, 2002). An asset is an electronic representation of media, more than
one asset can be collected together to build other assets. If this collection represents a single launchable learning object that utilizes SCORM RTE to communicate with a LMS, it is referred to as an SCO. The next component is called a Content Organization, a map that represents the intended use of the content through structured units of instruction (Activities). The set of Content Organizations is called Content Aggregation, to deliver it to the LMS, SCORM defines a Content Package, which consists of a compressed file containing the physical resources of educational content and at least one XML file —called manifest— that embodies a structured inventory of the content of the package. In our proposal, Content Packages are sent using the possibilities offered by Object Carousels. Content Organization
Content Package
Resources
Organization Item
SCO Asset
Item
Asset Item1
SCO
Item2
Asset
Item3
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Figure 5: SCORM Content Aggregation Model
4.2 The SCORM ontology The SCORM ontology we have developed has been inspired by the one for IEEE LOM presented by J. Qin et al. (2004), available at (Qin, 2002). We have extended this ontology so as the ontology we propose not only stores data belonging to LOM categories but it also permits the creation of instances for every SCORM element aforementioned and considers their interrelations. The main classes of this ontology, as well as the most relevant characteristics, are shown in Figure 4. The complete ontology is available at the web page http://idtv.det.uvigo.es/t-learning/SCORMontology. In this figure, we represent class inheritance with continuous line arrows and properties domains with broken line arrows. We can see that all the elements and relationships defined in SCORM are accurately represented in the ontology. We can thus observe that every SCORM element is a child of the class SCORM element, as well as the possible relationships between them. For example, a Content Aggregation is composed of several Content Organi-
zations, for supplying different ways of teaching the same topic to learners with different educational needs. These Content Organizations are in turn made up of Activities, comprising other Activities or resources: Assets or SCOs, which can both of them consist of other Assets. Concerning metadata accompanying these elements, each LOM category is encapsulated in a class and related to the elements through properties, as well Sequencing category for those elements which need it. In the figure, with reference to LOM categories, we have only shown some of the properties of two of them, those which will be mentioned when explaining the selection process in next section: Educational and Classification. Regarding Educational, it describes the pedagogical characteristics of the element, e.g. its interactivity level — which represents the degree of interactivity characterizing the element— or its difficulty —i.e. how hard is it to work with or through the element. Relating to Classification, it describes where the element falls within a particular classification system, its principal elements are taxon —to indicate the classification system used— and entry —which describes a particular term in that classification system.
5. Selection process Up to now we have explained the objectives of our proposal and we have exposed a tool which is necessary to achieve those objectives: the ontology for learning elements based on the SCORM standard. In this section we explain how t-MAESTRO has to use this one to built entercation experiences. When t-MAESTRO receives a Content Package in the Object Carousel, it unpacks it and uses the information it comes along with to create instances in the ontology, so as it stores instances of all the elements the ITS has access to in a concrete moment. In order for this to be possible, t-MAESTRO not only has to create the instances but also to delete those ones relating to the elements that are not available anymore. For this to be possible, it has to use the information in EPGs (Electronic Program Guides) —guides to scheduled broadcast television programs 3 — to store in the ontology the period of life of educational elements (to delete them when they expire) and how it has to recover them when they are needed. 3
We assume that the EPGs will be modified so as they include not only the information concerning TV programs but also the one for Learning Elements.
Figure 6: Instances of the ontologies and user profiles for the example
From this moment on, the Educational Recommender analyses the instances in the ontology to delete those ones which do not suit the Student Profile. To carry out this task, the most relevant LOM category to take into account is Educational, which contains information of the element with reference to its difficulty, typical age range, interactivity level, typical learning time, etc. It could happen that a course is not appropriate for the user as a whole, but one of its components is. In this case, the instances relating to those components will be kept in the ontology while the instance related to the course will be deleted.
Once t-MAESTRO has selected those elements which are more appropriate for the program, it has to recover and record them in the user’s PVR (Personal Video Recorder) together with the program which originated the entercation experience. Furthermore, using the information related to the segments of the program, t-MAESTRO should be able to indicate in a concrete moment to the viewer that there are related educational contents available from this moment on.
6. Creating entercation experiences: an example When AVATAR selects a program which can be interesting for the viewer, the Assembler has to choose —if possible— a set of educational elements —from those filtered by the Educational Recommender— related to those characteristics of the program which can arouse the viewer’s curiosity. To obtain these characteristics, it will use the information about the program stored in the ontology for TV programs exposed in Sec. 4. To choose the appropriate educational elements, those characteristics have to be mainly related to the information of these elements in the Classification LOM category using a Domain Ontology, e.g. SUMO, as mentioned in Sec. 3.
To clarify the proposed selection method, we expose in this section an example of the creation of the entercation experience we have already explained in Sec. 2. The process begins when AVATAR selects the film Under the Tuscan sun, since it considers that, according to the viewer’s profile, he will enjoy it. This conclusion was achieved given that the viewer’s favourite actress and movie genre —stored in his/her profile— coincide with the ones in the film, as we can see in Figure 6, where the branch of
the TV ontology corresponding to this film is shown.
able for a person who does not know anything in this language. If the student has the second Student Profile, the most appropriate element would be SCO121 because the user likes travelling and the content of this SCO is a situation taking place on a travel agency, that is why this SCO would be useful for him/her on a hypothetical trip to Italy.
t-MAESTRO, which has been populating the ontology with the information of the available learning elements analyzing the Content Packages where those ones are broadcasted, has in the ontology the instances for the documentary of Campania, the course of Art in Tuscany and the Italian course. In this example, we will see how the system selects the appropriate element in the Italian course to be shown to the student related to the film, according to two different student profiles.
Finally, the Assembler decides that the SCO selected by the Educational Recommender is appropriate for the film previously chosen by AVATAR, given that this film has dialogs in Italian, which is the content of the SCO.
The course has the structure shown in Figure 7: a Content Aggregation with two different organizations (which in turn consist of different activities). After analyzing the Content Package, t-MAESTRO has created in the ontology the instances for each of its components, shown in Figure 6. In this figure, we represent class inheritance with continuous line arrows, instances of a class with dotted line arrows and properties domains with broken line arrows. For the sake of simplicity, we have omitted the components of the second Content Organization in the figure.
We have implemented this Italian course, adapting its contents for IDTV. In Figure 8 we can see the SCO11, which will be shown to the user. It consists of a video with subtitles to teach the Italian greetings to the student. The elements of the SCO which show the information (in this case, the video and subtitles) should be synchronized. We manage the synchronization using contextual binding (LópezNores et al., 2004a). Playing controls are also offered, so as the student can repeat those parts of the video which are more difficult for him/her as much as he/she wants.
Figure 8: Example of learning object
7. Related work
Figure 7: Structure of the Italian course
After describing our proposal, we expose, in this section, some interesting work in the t-learning field, related to the proposal suggested in this paper.
After that, the Educational Recommender looks for an appropriate element according to the film’s characteristics and the information in the Student Profile 1. It concludes that both SCO11 and SCO121 are a good choice because their interactivity level is the one preferred by the user, their content is the Italian language and their level is in accordance with the user’s level. SCO11 is chosen because it teaches the greetings in Italian, very suit-
Some efforts to develop and broadcast t-learning material had been recently made by TV channels, however the produced material could loosely be called education and is best described as edutainment (pjb Associates, 2003). For example, some channels in the UK broadcast educational material for children, consisting in games and interactive stories; as well as documentaries with additional contents for the viewers to obtain more detailed
information, e.g. “Walking with beasts” produced by the BBC. A little closer to entercation experiences are the interactive video “virtual magazines” produced in France by Canal Satellite. Although they are not strictly intended to be educational, they are aimed at being informative and engaging the viewer and encouraging him/her to become active and find out more. A proposal to enhance TV programs so as they result useful in education is exposed in Fallahkhair et al. (2004), where the programs are used to support language learning. These programs are enhanced by complementary information sent to the user’s mobile phone. Although this approach coincides with ours since it also combines TV programs and education, it uses the programs directly as learning material, while in ours they are not educational themselves, but they act as an entrance door to education. Concerning the creation of t-learning courses, Aarreniemi-Jokipelto (2004) explains the experiences obtained from a course for a group of eight students of a Master Degree in Computer Science, launched on a set-top box and using a modem or ADSL as a return channel. It uses DVB-HTML 4 learning material based on text and still pictures. This work has taken a different direction than ours to create learning elements for t-learning. We conceive t-learning as a less formal form of education, principally intended for complimentary learning, not as the principal way to obtain knowledge. For this reason we consider that learning elements offered to the user should be entertaining for him/her to get engaged and mostly based on audio and video. In accordance with this conception, our working group has presented a model for t-learning services based on DVB-J 5 applications (López-Nores et al., 2004b), which describes how to structure, manage, control the presentation of the courses and assess the student’s knowledge.
8. Conclusions and future work In this paper we have presented a new concept related to education through IDTV: entercation experiences, where TV programs are used as an entrance to courses or learning objects related to the matter of this program. We have also exposed the appropriate scenario for the creation of those experiences to be possible, suggesting two necessary 4
DVB-HTLM applications are sets of documents written in a mark-up language that borrows technologies from the Internet. 5 DVB-J applications are Java programs that obey certain restrictions, related to the libraries they can make use of and the life cycle they must implement.
systems: AVATAR —whose mission is recommending audiovisual elements— and t-MAESTRO —an ITS for IDTV whose most relevant task in the context of this paper is composing entercation experiences. Finally, we have presented the key element for the creation of these experiences: a SCORM-based ontology which allows creating instances for the educational elements available that store their metadata and interrelations. The ITS, by inspecting this ontology, as well as the one for TV programs, should be able to choose the learning contents that best suit the TV program the user is watching. An interesting line of research with reference to education through IDTV is the application of Adaptive Hypermedia (AH) methods —whose objective is adapting hypermedia documents, i.e. those where diverse media are used and that allow the user to navigate through them in different ways— to learning experiences in IDTV. Masthoff et al. (2005) expose how AH techniques can be extended to time-based media, particularly in the domain of IDTV, e.g. dimming an item by showing it in a smaller screen portion when it is not particularly interesting for the viewer. We are currently exploring this line by defining adaptable courses for tlearning. t-MAESTRO is intended to create entercation experiences, but its mission could be wider, for example —in relation with the line of research exposed above— adapting courses according to the user profile. We are putting the final touches to a proposal of an extension to the ADL SCORM standard in an effort to permit a suitable adaptivity on the basis of user's characteristics. This extension comprises a syntax to express adaptivity rules based on a set of adaptation parameters. The actual values for these parameters are deduced from the user profile using inference rules. As a result, we obtain adaptable courses, created with the aim of being personalized before shown to the student. Similar approaches have been already presented, defining a new element in the Educational LOM category to encapsulate adaptivity information (Conlan, 2001) or suggesting the improvement of SCORM elements (SCOs and Assets), so as they can take different behaviours depending on the target user (Mödritscher, 2004). Moreover, closer to what we have presented in this paper, one of the tasks in the design of tMAESTRO to carry out in the short term is a formal definition of a user model, appropriate for the tlearning student, that is why it has to take into account both his/her components of viewer and student.
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